{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2e08925e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Init Plugin\n",
      "Init Graph Optimizer\n",
      "Init Kernel\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pathlib "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8134d8c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir='../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d2481bca",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_root=pathlib.Path(data_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "34e9b670",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_root"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "1c789d16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/lake\n",
      "../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/airplane\n"
     ]
    }
   ],
   "source": [
    "for item in data_root.iterdir():\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "89d583cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_image_path=list(data_root.glob('airplane/*'))+list(data_root.glob('lake/*'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "cf914bb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1400"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_image_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "25494c0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([PosixPath('../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/airplane/airplane_079.jpg'),\n",
       "  PosixPath('../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/airplane/airplane_045.jpg'),\n",
       "  PosixPath('../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/airplane/airplane_051.jpg')],\n",
       " [PosixPath('../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/lake/lake_039.jpg'),\n",
       "  PosixPath('../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/lake/lake_011.jpg'),\n",
       "  PosixPath('../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/lake/lake_005.jpg')])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_image_path[:3],all_image_path[-3:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "af9603f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_image_path=[str(path) for path in all_image_path]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a5641ec0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/airplane/airplane_092.jpg',\n",
       " '../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/airplane/airplane_509.jpg',\n",
       " '../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/airplane/airplane_521.jpg',\n",
       " '../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/airplane/airplane_247.jpg',\n",
       " '../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/airplane/airplane_253.jpg']"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_image_path[4:9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "136753ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "1cd74c92",
   "metadata": {},
   "outputs": [],
   "source": [
    "random.shuffle(all_image_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d7957eb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/airplane/airplane_114.jpg',\n",
       " '../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/airplane/airplane_103.jpg',\n",
       " '../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/airplane/airplane_454.jpg',\n",
       " '../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/airplane/airplane_676.jpg',\n",
       " '../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/airplane/airplane_622.jpg',\n",
       " '../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/lake/lake_201.jpg',\n",
       " '../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/lake/lake_651.jpg']"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_image_path[2:9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "b5f7024d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1400"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_count=len(all_image_path)\n",
    "image_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "5469c702",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['airplane', 'lake']"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_names= sorted([item.name for item in data_root.glob('*')])\n",
    "label_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e27df056",
   "metadata": {},
   "outputs": [],
   "source": [
    "label_to_index=dict((name,index) for index ,name in enumerate(label_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "9cd2bf85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'airplane': 0, 'lake': 1}"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_to_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "2bda5977",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'lake'"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pathlib.Path(\"../Desktop/data/2_class/lake/lake_651.jpg\").parent.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "cdc7e2c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_image_label=[label_to_index[pathlib.Path(p).parent.name] for p in all_image_path ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "af0a6f7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 0, 0, 0, 0]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_image_label[:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "ef7c2cf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/airplane/airplane_525.jpg',\n",
       " '../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/lake/lake_335.jpg',\n",
       " '../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/airplane/airplane_114.jpg',\n",
       " '../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/airplane/airplane_103.jpg',\n",
       " '../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/airplane/airplane_454.jpg',\n",
       " '../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/airplane/airplane_676.jpg']"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_image_path[:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "c7c21726",
   "metadata": {},
   "outputs": [],
   "source": [
    "index_to_label= dict((v,k) for k,v in label_to_index.items())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "8404f1a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 'airplane', 1: 'lake'}"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index_to_label"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de7144c1",
   "metadata": {},
   "source": [
    "# 解码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "b2baf17f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import IPython.display as display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "a88da669",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lake\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "airplane\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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kuVxjbxUi9PWoLDAI6VGcY4HPWpAuCTUVxvCb4zgj1pALG+4fKDTvmPGKbC4GcDg8giptp5piKzRE5B5BrOjHkXDIc4zmth8KAcis6/jziZeg600xFyF8gcc1ZHX3qhav5kYOfmFX43z1GDQxoJW/dE+gpgAJ4NO2ncyt0PFMGI8A+uK5zdE5hDoSD9KWBsKPrTQxxw34VHC22RgcnJqLWZZm6jDsuHGMDqPpWYy/MR+NdDqkJZElHYbTWG4xye3WumD0MJrUYDhfY1NE+1lcdVOah2lQR1x0qVAcDJAqzNnodg8c1nHMgGHXNVrhSt5jOF61R8PXX+hG3B5Rs4PpWjdjgSUiWRXUZfgDIIxzXMWyfZr+SE/xEqB711aNviBrnddhMNzFcoCAeuPUUFItwqQ4B6mppGCkHsapxXAcK4PUZ/xq+wWWLNQyimwXzHTJB6g1Xyu44PTrU5DBzwMDgetQtGWfI4XtjvQnYCCb25qCQlhkdavsvGBUSwjBBqkwKETeXdDPQ8HNX3gDDGeD6VHJbB2GP0rUgWAxKXKg4wfWnzEWOYubQPcrDbJGkjdZGXJro9Ls2062HmTtKx5J7A1GRbRymSOPc/8AeJpst1K64PA9KTdx2sSXZAOSQzHkgVUDHcDT0ieQ5APHtUe7GQRkjrij0KLSHnmo7oI0ffjnPpUQcnkcCnOwx6g0WARJlRcDPFSGUkYqEoAMjoRTlb5OaBAw3Eg0hQNE0Z7ilIyR7UAfPmhaAUbY+TKUbjHGK1UG4D1NZ13GUcTAfWr1o4dRnrTeqEXGHzZqJxlDkd6nPK/SopMFc+lc50EarkVG52SKfzqQEjntSTAFM0mOLJZYWks2Xcema52VRkj1FdHay7owp+lY1/D5U7rgYzuH0NaU30JqLqZ+8hFAPsTS+h601xgkevIpAwC/NW6MTW0e7EGoJuxtkG05rssrPblcnoc+1edQyZYY6rzXcWd8n9nJMSF+TnJ/z6UpE2EtXYgjnHTB6iqOsK0tu0RIDD7vucZp63Lnybi1t5ZxLklIzn5h79BVe+tdVuXSSaG3gxjvvZR9f6D1qLhaxl6bKzIyMeUOfwNbVq52GM9qxTEbHVvLbhZD19jWyg8uVSfXBNDKFlQEMCTgjtUEQ2xgckDjmrcycD3qovyyFT3pDFaMNznmmY2tjHFTqB3prLzTAayAITmqpk/eYRdxIxgVNLJlcVTWZbaQSkgAHqadgL0dlNJy3yDPar+p6HBokdrNJcSTQ3Ee8SSYGG7imm7Ty1YnrzxXSXNtB4n+HIAjjmmt923eOhU/pwaaVzOUjl7XUrOcmKOVGAHIWsq9ZLW9IOAr9MHisHQNP1x74pp2nS3CFtvmIh2/metbeqaXqqyLFqtkISo3ja4JH1xTcbMalckDAjHanKARg1HEFCipeCcdqRQnJyowOO9IMhsGjOH96cwyKQCgU5vug0xOVp/UYoASSMSwtH6jiqtlIUfaeoNWx2I7VTnUw3Yb+FuaaA3MHHTioc7iVPSpN/rTCMPkdK5zcixkHPQGgn93yKV1+8ucZ6U1R8gUkkikCIY3ZJOTx1pNTjDRpIPTGac2Ax46VO8ST2jqO4oi7MqS0Ocb17iozGT05HXmp5Vw+D3HP1qEthRz0OK6Vqc9gRAjg569cV0GhrBcSNFKgcqMornIHrxWARxx1FXNOnNvdxyg45wabROx2scbMsm+QgA7owvyhR6YqykcciHgn2NVN4ZRjlTyKls7jzdwYBGU4ZRWdhMwfE1ofLjuBnK8NUdvOLi2jbdnjDH3FberJEV8qQ5EowBjODXJ6c5inltm69R9RVFI30bfD64qrOMYYdRToHxIVz8pFLKhxz+RqRkQfuD9eaPNyvGKqqskW4BQT1FSMWdcbecc4pgRyNyQTUEEkMd3GbmIyQK2ZFGCSvfGfanMp5BFRSR71Hbnr6VQnsdDb+E47geZJqstnpZ+aISxbZCPqcD8hXU+F7jRdMnbTNLnlmDZdmkGdw7/ANDXE6jrB1EWIbzPNhQpISeD6Gn2Mk1teRXMeQ0bbgVODTciLFrxj4u1jR9We0tHtbG1/haOL59pweSR79qp2l82pRee8hnZxhpG6tXQ+OrODVI9P1VIEkhlQpISOjA5A/VvyrnoTDBCFRQm3oqilKV0EUVTHskdD1B4o5HB6Us775twUj1pFBzz3pI0FA3ZpRnaQacvysRSnpj1oAjHAyKdSbeo7U8gdutACcDtUN1H5lvkdVqwVO0ZpoByR6ihAWsZFL060o6CnMOBgVgzYZ3HANVmISUDPWrOBiq9wnCuDnB6CgRHMOAQMmp7B15HvUe3A28nIznFNhPlyY70i1qijqsBiunxjBORisyQAn/erotVj3wJIBkjg1z0oxx3B4rog9DCa1GI3yj171JGx5waj2ZJ2tgH1pRtBB6+tXcg6201KFNNWSSUKVBXa3UkdKckl89vBNboiSOSrGZdq465rN8PSR/birhSWXCk/wAP0rovJiDyNKN2/wDvHO36VlJMNChdafe3ttvuL/dgZKRJtHXt/jWHqkZsNRinVdqMB68e1dVaZA2EjgcCsrXrY3Fo+F5jO4H2qkguRowKCRDxw1TMdy5JyfesvTZFeyCnqhx+FXomyu09aVikMkTy24JO40KPSrMiZTjkgcVWXGSM8g8igCJ1w31pIkzIUPQ1ZdQRzUAwr+9PcBZIli2j86sfbWSICOIZHQmm3RV1ViO1RWkf2iXyy2FxQF7oml1u9ewNo8jGDdv2gdO3H51UtrmOe4MCZMgxxits2FvHCRjPoWpxRbrSp4Yowt1blponTALHGNv4gVLYtil9kMkZDMAccAVRTrs6FeDUWna0bu6ZZIvLl/3s596dfSfZ5wRkhvSqQXRZkI25FNLZAxUcb5Uj15oQYzQMlyT2+tKO/qKjySMU6P0/CgB4LHr0obggin42/SkIBxSAlDYJz2qTdkAD0qJueQOaWMkjmsTYewzULE8jHUZB9amJqK5B2bwfu80LUCKBvM3A/wAPaq88UySqyEbScnNCzbZGcHhucVNvDx59aJIcWWmiM9uUHQjiuanRkmKsO+K6K0lIOw9/0rN1eEiTcB97mqpsmoupisCOPQ05cHNOZcsO4IqIferoRiXLWZoJo5F6hq7dVa5iWTOFYZrg4yCNua7HQbwTaeYc5eM4/DtSZLL8NuitkEkilktxKjqTgEYNPiJ3EHANSlR2qRHDxxmx1Sa3blTkD+lXkyHHvxUviS08uWK6XOOjH3FQiQSRB0P3hnj1FDLTLseGGfSqxAErDGCaljk4BplwMFX98VI7CZzxVeYAHAqUdfeopRupoQiOZYSrdVqGKc286upxznNJnZJkVFIwcHnA+tOwG5d3bQwrPIIxBKf3bhuQcZwRUPhvW4o/EIjZPOgkHzMVJVcd81Ho8K3YnhluFWDy8tF5QZpCDwMnpVhNReO0SCC2hjVXznHJHpUtdgK+p+D76XVbvVba4jsrMNlHJLscn34H0pbnQ1gPmW+oyXoVQWY/dyfStfxdAdd8P2d8i4W3+UKGOGP06fzqDSC72MYlXZldpjNSmxJamPGcqDj6inZ5qSWL7PdvEeh5BpAoGc1oMQrgAigHB4pyZPHagrgmgCUHcBnrTgBxxUKtg81MDzxSGL1JpAPn4oQ7ge3OKHBD47VibD93OKQjKnJyMYoC569aRT8+O1AGeY9uFboDwRVuGMMoBHyimTACUAjrSoxDHHFEk7aBF6ijEVxtXue9LqsO+2D9wcmoZATKG59zV+NhcIYm5JGKUdCpq6OUcfeX0PBppQEBsgZqeeMxyEEcg4NV2JVCOwOa6U7nO9BVCqeDmtzw/c+XqCox+ST5cdOawxyuRU0MhVlYNjawPWnYlnoTgRvlaQP1yeDVS3uftVssoP3hn6VGZSlyBI4KNwPY/wD1/wClSSSalCt3YyRDJYDcv1FczYSHa0JxuQkiuq3eWjnGQFziuTuDHBqvmRNmNz27DvTsNF9H5I/EVI7o8Jz075qucK/XHb8KsqADjHWpLKgIf5d+D1AHpT2j3L3olAScHaMGpVGQR+VAFGVAhx3FJPHm1LxJkgcj1qzMmfmotxkshPBp3CxW0mea3vI5Y8xsO/1rWksndWII3nJyB39aqRQ+Wcnk1Ya4lZcFyo9qXW4i3pdsI7K5sr64why8Q/un0qAXuxQoGSO/aqUhdjxkgdzzRaRXM900Utu0aqMqz/xj1FJoew+4d5234yw9KRfmCn1rUS1jUFT8xx6YrLQHdJH0IPemncB6ja3tQ5xk0IcjB6imNzlTTEAA60/05qFSFPPQVOduAR09qQySSMoxHTnkUm7OPapbnPmsT0Peof4eKxNR4bIpHXGD3pIySTnp2pzcigZVu1LoGU8jrSJ90E96mYZHsKgZthzj5R1prUTHt2IH1qa1I8yo+B7g80kbiOYY71L0LWxT1a32SBx/FWS4zjPfiun1KHfaFhyRzXNOvJ/StqbMZIhBK5HpTlI34zTlXf8AMCB65pu1Vkz/ACrUg6LSdSSGxkWRsbG49efSr0KXF7DcHy3iVvmieTA5Ht1rC0mdIr+IuqlC3Of5110oVp1mGGZc4yM1nJNCMp9OaeIfabx5ZCOFhGEX8+TVHUNLFtp6mFHVY2xluS2e9dAyqTuTgZ7U25KyWrwsMhhjmmg0MGB1ltEk/iA2mrKPlRxyKyYZmhklt8ZJHT3FXoWOMHvQ1YaZYlXem7uKSNxuWpAMpg1XHynGeQeKQyWVODVcYV+Ksq29MHrUEi4NAE+cL+FLZRxXE+JgSOwzVTzCyYB5WhZ/s7iRT93mhrQR0XlxxrtVFXjBHWmXU0t9o80abBcWxzbsVJxyMqcc8jNZd3qRtEEzzJLDIuRsH3D/AHSas+Ftau4tWMkllJ9jlQ4LpwT0yPWpYXM7T9XuJC8V40aSI2CNpU/kee4pL+UJNFLHhg/BwR1qXWPCVrZu19qF7Hbm4kBFvH8h2Z6885qy+iaYf3+lxPHHt/jbOT61SaYrlQFgenWlVTupsYKJhsEg8kVMPmFMZAcBiDUykYAHQdqZKuRkCmA7R15pDNG5X5QfSqrHitCVQcg9DVDHb0rI1WokfTFO9qP4aP4qRSEYYWq7jcDx2qyeTio3G0HAoTE0V4chSj59jRklxntT2DED25FNdOpz+FNgtzSQiWEr1BFczeJ5c7LjGDW5aTbk2lcFap6vCBJuGPmHX3FOm7MU0YpJwy/jSlcj60rL0NIAQOtdBiPj+QgjtXZafJ9rskkDfMBj8a4xWBNdH4duAoeDPX5hSZJrYdRgcCmmJWOS1SO2Acd+9RKSen8qlAc9q8Is9RWZfutzx6ip1IDblPB+YZq7rFkbiwMg+8h3f41l2L77YKT88Z5+lNjRoA+nfvTZlUFWI74z70kZ+Xb6UsrKEJY8f1qLWKuRqD+dNkXK4J5p4VCoZsjB7U54/l3evancCnuxJ+lIycFW5B9Kc6c5xUi/MoPpTAuaFYw3AukkklkfadlsFwrH++T7elW5NRv1tooAVRICCsYUYGD3z1rPs2ktrhZI3dCQVJU9jV8wF1O5uo61LEXfEaS614bi1KV4C6PtVCoGAPfHTOKz9Kjljtl+0N83cdhU9m9pbQzW19K7QScoqDJRsdf1qsb1RlYVIUcAn9KSCwy5jWO5I6K/I4qIKRx6UydnncNJITjp7UoOAH5I71QxSf4W61FKuCDU7hGXk+9V3bAx/OmBsP8AdUiqMqlZGFajoAhAqjcocK4/GsTQrUo+9RQnekNEmOKjcZqUdKYRyTQMhPK4phQkdalAGTk80EALxQBXsyEuHUk59TVi/i324YdRz/n8KruNn7wdfWr8O2eA5O7I5pLcb2OXlGAQOh6U0IWUFfoatXSBZHXGNrY/CqmSAQO9dKZgxAu3qRV6wnEN3E3AGcE1QBxUkZ6E9QadiWdw4yoI5BpiuFBDHHpUWm3IuNPUk8jg1Mtt5zHJqdhDnZfI2Z+UjmuWi/0TUHiboxxzXTm12thjWFr1uIpUuI847+xp3BDlciTn6VP25qsj70VgOGGfxqyq7wCKllhECZdrAbTzmpSMZXrjpTXjHl/MMjPXpSrndxk1NwIJU61EPl5Aq4VDKd3Wotox0qrgNDnIPSpvtEhAU5xUBHSpApAxntQApYE5xSfSmbmD+1Sh+cAUrgN2EjLH8qdnGQBnPYVNHblzlzgelSuixAbFouOxXSDcMsTj0qK4VVHAqxJKFHBrntV1+2sgwZvMfHCKaaTYNo7PquarTKNjr3xkGppG2fQ0kijCt2PWsmijLUnbz1pV4P1qWZNkhHY1DSKJqaaVfuikb6UDRAw+Y0EYFOf2puSRQA0lTE4ZegpdMmUoVHQnFMchTyOMHI9arWLLFcEbyFPSgodqkQEocD7wIP4VkyLnDD8a6K+i823OOq8isFgMdOD0rSDMZ7lc96cDhhQVJH0o2HFa3IZu6BNiZoS3D8gGuki2pJuPOK4ixm8m5jkB5BrsYyZF3Bs554pMksXMityCAKytRtjc2Mm3JK8jFaflhozxzUceBlX6cipugOX047omiY/MpyK0I8jgdKoSr9h1Rh/CTx9DWguQRTZaZOqDBBPBpwH7okAfhTFOTigHDsnYjNSAgwfxprDk0DilI/WkFivIh2nBxT0GVXPXHNSKFyc9DSEbW46UbANdM9Kfb48wA4zjFOC5wajkykgdc8UXAtyOE56GsfU/EFnYxnfKHb+6vWtK4tmv7LGGj3jGQeRWTaeEtLt23So1y/cyHI/KmrdR27HMXmtanq4MdnFLtbgbR1+ppbLwJqN0qvezLArdeckCvQoooII1WCFI1HZVxTh94jsaPadgcT//2Q==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "airplane\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for n in range(3):\n",
    "    image_index=random.choice(range(len(all_image_path)))\n",
    "    display.display(display.Image(all_image_path[image_index]))\n",
    "    print(index_to_label[all_image_label[image_index]])\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "1f93d9d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/airplane/airplane_525.jpg'"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_path=all_image_path[0]\n",
    "image_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "d7fb8f83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metal device set to: Apple M1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-08-01 11:42:02.718346: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
      "2021-08-01 11:42:02.719246: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n"
     ]
    },
    {
     "data": {
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      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_raw=tf.io.read_file(image_path)\n",
    "image_raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "24328caa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([256, 256, 3])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_tensor=tf.image.decode_image(image_raw)\n",
    "image_tensor.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "69976313",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tf.uint8"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_tensor.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "deeed581",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(256, 256, 3), dtype=uint8, numpy=\n",
       "array([[[115, 116,  98],\n",
       "        [112, 113,  95],\n",
       "        [113, 111,  96],\n",
       "        ...,\n",
       "        [168, 160, 141],\n",
       "        [174, 163, 145],\n",
       "        [176, 165, 147]],\n",
       "\n",
       "       [[109, 110,  92],\n",
       "        [112, 113,  95],\n",
       "        [120, 118, 103],\n",
       "        ...,\n",
       "        [169, 161, 142],\n",
       "        [175, 164, 146],\n",
       "        [177, 166, 148]],\n",
       "\n",
       "       [[121, 122, 104],\n",
       "        [123, 124, 106],\n",
       "        [127, 125, 110],\n",
       "        ...,\n",
       "        [171, 163, 144],\n",
       "        [177, 166, 148],\n",
       "        [179, 168, 150]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[172, 165, 146],\n",
       "        [173, 166, 147],\n",
       "        [174, 167, 148],\n",
       "        ...,\n",
       "        [ 96,  89,  73],\n",
       "        [ 96,  89,  73],\n",
       "        [ 93,  86,  70]],\n",
       "\n",
       "       [[177, 170, 151],\n",
       "        [176, 169, 150],\n",
       "        [176, 169, 150],\n",
       "        ...,\n",
       "        [100,  91,  76],\n",
       "        [ 99,  90,  75],\n",
       "        [ 95,  86,  71]],\n",
       "\n",
       "       [[182, 175, 156],\n",
       "        [180, 173, 154],\n",
       "        [178, 171, 152],\n",
       "        ...,\n",
       "        [ 85,  76,  61],\n",
       "        [ 91,  82,  67],\n",
       "        [ 99,  90,  75]]], dtype=uint8)>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_tensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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